Complete autopsies of 9 deceased COVID-19 patients (5 males and 4 females) with 15 median hospitalization days (IQR, 10-22) before death were performed (Table 1). The median of ages was 67 years old (IQR, 63-78). Except for the missing comorbidity records of 2 cases (cases 6 and 9), the other 7 cases all had comorbidities. To be specific, 7/7 cases had the comorbidity of hypertension, 2/7 (cases 1 and 4) of cerebral infarction, 2/7 (cases 5 and 8) of coronary artery disease, and 1/7 (case 7) of gout. Of note, one case (case 5) had not only hypertension and coronary artery disease but also renal dysfunction, lacunar infarction, and chronic bronchitis with emphysema. 8/9 cases died mainly due to the respiratory failure with multiple organ failure and the other 1/9 died due to sudden cardiac death from acute coronary heart disease.
Besides the diffuse alveolar damage in the lung, the predominant histological findings were hyaline thrombi among all the 9 deceased patients (Figure 1). To be specific, 9/9 cases showed microthrombi in hilar arteriole, alveolar wall capillary and interstitial vascular lumen of the lung, 4/9 (1, 2, 3, and 5) in the subarachnoid arteriole and parenchymal small endovascular lumen of the brain, 4/9 (1, 2, 3, and 5) in the small vascular lumen of the spleen, 2/9 (cases 2 and 9) within the kidney, and 1/9 (case 4) in coronary artery lumen together with hemorrhage. To evaluate the coagulation state before death, we also extracted the last hematological indices relevant to coagulation, i.e. platelet, prothrombin time, activated partial thromboplastin time, thrombin time, fibrinogen, and D-dimer in these cases when they were alive (Table 1). The ISTH DIC scores in 8/9 cases matched the grade of DIC (≥5 points).
The autopsy results of thrombi in the major organs of the body and their DIC before death strongly indicated coagulation abnormalities in COVID-19 patients (Table 1, Figure 1). Together with previous reports showing the high relevance of blood glucose, total cholesterol, triglyceride, high-density lipoprotein, and low-density lipoprotein with coagulation,30-33 we then included those indices in our clinical data analyses. The clinical time-sequential data included 407 hospitalized patients with confirmed COVID-19. Their demographic and clinical characteristics were shown in Table 2. The median (IQR) age was 62.0 years (51.0-58.0) with the overall range from 6 years to 92 years; 51.8% of the patients were from 40 to 65 years of age. A total of 49.9% were female. Of all the patients, 184 (45.2%) had at least 1 of the following 3 comorbidities: coronary artery disease (40 [9.8%]), hypertension (144 [35.4%]), and diabetes (72 [17.7%]). Since all the patients included in this study had either been discharged from hospital with SARS-CoV-2 negativity (368 [90.4%]) or deceased (39 [9.58%]), the median duration of hospitalization was countable at 15 days (8.0-25.0).
Among all the 407 patients with their different symptoms throughout the hospital stay, 253 patients (62.2%) showed mild infection, while 73 patients (17.9%) showed severe infection, and 81 patients (19.9%) showed critical infection. All relevant clinical records were reviewed to classified the patients into critical, severe, or mild groups according to the Chinese National Health Commission (NHC) guidelines (7th trial edition) for COVID-19 pneumonia.26 The patients with critical or severe infection were significantly older than those with mild infection (median [IQR] age, 65.0 [57.0-72.0] or 67.0 [60.0-73.0] years vs 59.2 [45.0-59.0] years; both P values < 0.0001) and more likely to stay longer at hospital after admission (median 19.0 days [7.0-35.0] or 21.0 [14.0-26.0] vs 13.0 [8.0-22.0]; both P values < 0.001), while there was no difference between severe ones and critical ones (P = 0.5996 for age and P = 0.2806 for hospital stay). Moreover, compared to the mild infected patients, patients with severe infection were more likely to have other underlying comorbidities (47 [64.4%] vs 98 [24.1%]; P = 0.0001) especially hypertension (36 [49.3%] vs 77 [30.4%]; P = 0.0028) (Table 2, Table S1).
However, those 81 critical patients showed slightly close features of the underlying comorbidities to the mild ones (39 [48.1%] in critical ones, P = 0.1339) and no significant difference of hypertension proportion between them (31 [38.3%] in critical ones, P = 0.1894) (Table S1). Furthermore, despite no statistically significant difference (P = 0.2806), the median hospitalization days of critical patients were slightly shorter than that of severe ones (19.0 [7.0-35.0] vs. 21.0 [14.0-26.0]). This led us to think about whether the non-survivors displayed any difference in the critical group.
Table S2 demonstrated that when compared with the survivors (42 (51.9%) patients in critical group) in the critical group, fewer non-survivors had underlying comorbidities (13 [33.3%] vs 26 [61.9%]; P =0.01), such as hypertension (8 [20.5%] vs 23 [54.8%]; P =0.002) and diabetes mellitus (3 [7.7%] vs 12 [28.6%]; P = 0.02). 39 non-survivors also had shorter hospital stay after admission than that of 42 survivors (median 10.0 days [6.5-16.5] vs 35.0 [21.3-40.5]; P < 0.0001). In addition, those comorbidities percentages of comorbidities and hospital duration in survivors were more similar to the severe group than those in non-survivors from the critical group (Table 2).
To determine the major hematological features that appeared during COVID-19 thrombogenic progression, the temporal changes of 11 clinical laboratory indices, including platelet (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen (FIB), D-dimer, blood glucose (GLU), total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL), were tracked on admission until outcome (Table S3). All the 407 patients with definite discharge status were analyzed and displayed using the line chart (Figure 2). During hospitalization, most patients had increased D-dimer, and those with critical infection stay significantly higher D-dimer after admission until the outcome. Intriguingly, PLT in the critical patients showed a marked down on admission, kept the counts low until day 14 (both P < 0.05 compared to mild and severe patients [Table S3]), and then gradually increased. In addition, the indices, e.g. PT, and GLU in critical patients showed persistent prolonged time, higher score or higher level during the hospitalization than those in severe or mild patients, while others, e.g. TC and HDL in the critical patients were lower at the initial stage and stayed a relatively low level until the outcome. On the other hand, indices e.g. LDL exhibited their changes at the late stage and TT was intermittently prolonged after admission in the critical patients while APTT, FIB, and TG had no discernable difference among patients with different levels of severity during hospitalization. We also found the persistent high DIC score in the critical patients during whole hospitalization using ISTH DIC scoring system.
To evaluate the severity of the coagulation state in patients with different disease levels, we also ought to evaluate the percentages of 11 indices’ peak value in every individual that ever reached out of normal range during hospitalization. Table S4 and Table S5 summarized the median (IQR) of the level of all the maximum values of 11 indices in every individual patient during the hospitalization, the proportions of out-of-normal-range values of all the patients with different levels of severity and their statistic test results. In line with the previous findings4,34, the median concentrations of D-dimer (μg/mL) in all types were higher than the normal range (all patients, 1.15 [0.39-3.18]; normal range, 0-0.5). Nonetheless, critical patients showed the significantly higher level of D-dimer than those with the other two types (critical median [IQR] vs severe median [IQR] or mild median [IQR];16.77 [3.21-12.9] vs 1.60 [0.78-2.94] or 0.53 [0.28-1.39]; two P both < 0.0001). Other coagulation parameters i.e. PT (17.9s [15.6-21.9] vs 14.3s [13.9-14.8] or 14.0s [13.4-14.5]; two P both < 0.0001), APTT (52.9s [44.1-68.7] vs 43.6s [39.9-47.9] or 40.5s [37.8-44.0]; two P both < 0.0001) and TT (20.0s [17.3-27.0] vs 17.4s [16.8-17.7] or vs 16.8 [16.1-17.8]; two P both < 0.0001) were also prolonged in this peak value evaluation. Consistently, the out-of-normal-range portions of those coagulation parameters in critical patients were significantly larger than those in severe or mild patients, e.g. D-dimer (79/80 [98.8%] vs 64/72 [88.9%], P =0.0101; or vs 127/247 [51.4%], P < 0.0001), similar in the DIC grades.
Our autopsy results and 81 critical patients strongly suggested the difference existing between survivors and non-survivors, so the progression analyses of laboratory hematological indices were also taken to evaluate the severity of coagulation. Since the destinations of all patients were confirmed either discharged with SARS-CoV-2 negativity or deceased, we defined the date of discharge or decease as day 1 before outcome and the previous dates increased backward (Figure 3, Table S6). Interestingly, most indices of survivors and non-survivors shared similar trends, medians, and portions of abnormal values with the whole critical patients. However, when dividing the critical patients into survivors and non-survivors, several indices exhibited a significant difference between them. Combining analysis with the maximum values of individual patients, non-survivors presented with fewer platelets (×109/L)(216.0 [142.5-273.5] vs 287.5 [199.0-370.0]; P = 0.0035), prolonged prothrombin time (20.2s [18.1-25.3] vs 16.7s [15.3-18.0]; P < 0.0001), elevated D-dimer (21.00 μg/mL [13.42-21.00] vs 6.79 μg/mL [2.82-20.61]; P = 0.0013) and DIC score (6 [5-7] vs 5 [5-6]; P = 0.0002) than survivors (Table S7, Table S8), strikingly when it came close to the destination date (P = 0.0027 and P = 0.0051 for PLT and PT at day 11 before outcome, respectively; P = 0.0063 and P = 0.0193 for D-dimer at day 12 before outcome)(Figure 3, Table S6). Notably, while no obvious change could be found when divided all patients into 3 groups (Figure 2), the subgroup of non-survivors manifested a significantly higher level of fibrinogen than that of survivors at days 7 and 9 before outcome (Figure 3, Table S6).
To further explore the underlying correlation between these groups, the heat map was applied to visualize the Pearson correlation coefficient between each clinical feature or laboratory indices (Figure 4). “Severity” in the heatmap indicated the severities of COVID-19, i.e. mild, severe, and critical classifications. As indicated by the heat map, the features that positively correlated with patient classifications included coagulation indices e.g. PT (Pearson correlation 0.46), APTT (Pearson correlation 0.31), and D-dimer (Pearson correlation 0.46) and others e.g. GLU (Pearson correlation 0.42) whereas indices including TC (Pearson correlation -0.42), HDL (Pearson correlation -0.54), and LDL (Pearson correlation -0.54) showed a significantly negative correlation with patient classifications. We further applied those data to the normal distribution curve to estimate those features’ relationship with the severity (Figure 4). Unlike age-severity distribution with the critical group’s mean between severe group and mild group (Figure 4), coagulation indices-severity distributions including PT, APTT, and D-dimer all complied with the mild-severe-critical distribution positively and other indices such as TC, HDL, and LDL negatively (Figure S1). To explore which indices played an indispensable role, a random forest model was constructed according to patient classifications. The best accuracy of the model is 83.8%, the maximum depth of the tree is 9, and the number of classifiers is 50 (Figure S1). Then the model showed us the importance of each feature (Figure 4, Figure S1). The most important feature was PT, followed by D-dimer. These two features contributed to the 40% importance of total. The red dotted line together with the black one separated the features that totaled 90% importance. Taken together, those data suggested the important role of coagulation and hematological indices during the deterioration of COVID-19 progress.